How can you team up with machine learning experts on GitHub and Kaggle?

1 GitHub basics

GitHub is a web-based platform that allows you to host, manage, and share your code with other developers. It's based on Git, a version control system that tracks changes and history of your code. GitHub also has many features that facilitate collaboration, such as issues, pull requests, forks, and stars. You can use GitHub to browse, clone, or fork repositories (collections of code and files) created by other machine learning experts, or create your own repositories and invite others to contribute. GitHub also has a social aspect, as you can follow, comment, or message other users, or join organizations and teams.

2 Kaggle basics

Kaggle is an online platform that hosts competitions, datasets, notebooks, and courses related to machine learning and data science. It's a great place to learn from and compete with machine learning experts from around the world, as well as to showcase your skills and projects. You can use Kaggle to access and download thousands of datasets, ranging from images and text to audio and video. You can also use Kaggle to create and run notebooks, which are interactive documents that contain code, text, and visualizations. Kaggle also has a community aspect, as you can join, comment, or vote on competitions, notebooks, or discussions.

3 How to find machine learning experts on GitHub

One of the easiest ways to find machine learning experts on GitHub is to use the Explore feature, which lets you discover trending topics, repositories, and users. You can filter by language, such as Python or R, or by topic, such as machine-learning or deep-learning. You can also search by keywords or hashtags, such as #tensorflow or #pytorch. Another way to find machine learning experts on GitHub is to use the Stars feature, which lets you see what repositories other users have starred (liked). You can follow users who have starred repositories that interest you, or check out their own repositories and contributions.

4 How to find machine learning experts on Kaggle

One of the easiest ways to find machine learning experts on Kaggle is to use the Leaderboard feature, which lets you see the rankings and profiles of the top competitors in each competition. You can filter by category, such as Featured or Playground, or by time, such as Current or Past. You can also search by keywords or tags, such as image-classification or natural-language-processing. Another way to find machine learning experts on Kaggle is to use the Notebooks feature, which lets you see the most popular or recent notebooks created by other users. You can filter by language, such as Python or R, or by topic, such as machine-learning or data-visualization. You can also search by keywords or tags, such as #keras or #scikit-learn.

5 How to team up with machine learning experts on GitHub

One of the best ways to team up with machine learning experts on GitHub is to contribute to open source projects. Open source projects are repositories that are publicly available and open for anyone to modify or improve. You can find open source projects related to machine learning by using the Explore or Search features, or by checking out the Awesome lists, which are curated collections of awesome resources. To contribute to an open source project, you need to fork the repository, make your changes, and submit a pull request. You can also communicate with the project owners or maintainers by using the Issues or Discussions features, or by joining their chat channels.

6 How to team up with machine learning experts on Kaggle

One of the best ways to team up with machine learning experts on Kaggle is to join or create a team for a competition. Teams are groups of users who work together to submit solutions and share prizes. You can find teams for a competition by using the Teams feature, which lets you see the existing teams or create your own. You can also communicate with your team members by using the Team Chat feature, or by sharing notebooks or files. Another way to team up with machine learning experts on Kaggle is to join or create a study group for a course. Study groups are groups of users who learn together and support each other. You can find study groups for a course by using the Study Groups feature, which lets you see the existing groups or create your own. You can also communicate with your group members by using the Group Chat feature, or by sharing notebooks or files.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?